Data analysis method and device for space state expansion of wind turbine generator

Abstract

The invention discloses a data analysis method and device for space state expansion of a wind turbine generator, and relates to the technical field of electric digital data processing. The method comprises the steps that a unit state equation is listed for unit state quantities, and meanwhile, nonlinear time-varying items of key parameters such as filter inductance, capacitance, line impedance and power grid impedance are introduced; adding model error terms introduced by unmodeled parts such as a dead zone, power tube internal resistance and voltage drop, parasitic parameters and the like, considering random disturbance terms such as state sampling errors, external disturbance and the like, and establishing an extended state space model of the multi-class new energy unit; partial non-key state and control link influence are ignored, and a new energy unit order reduction model for balancing transient stability analysis rapidity and accuracy is established; and combining a Lyapunov stability theory to summarize a transient stability analysis problem of the system into a maximum attraction domain estimation problem of the system, so that accurate data support is provided for subsequently exploring a mapping relationship between the maximum estimation attraction domain and system parameters and state response characteristics under typical working conditions.

Description

Technical Field

[0001] The present application relates to the technical field of electrical digital data processing, and in particular to a data analysis method and device for spatial state expansion of a wind turbine generator set. Background Art

[0002] In recent years, with the support of policies and technology from countries around the world, new energy technologies such as wind and solar energy have made significant progress, and installed capacity has continued to rise. With the maturity and large-scale application of new energy technologies, their economic benefits have gradually emerged, and the unit power generation cost of new energy such as wind and solar has been significantly reduced, which has accelerated the process of energy structure transformation. The proportion of new energy has continued to increase worldwide, and the way of energy production and consumption is undergoing profound changes.

[0003] However, the development and utilization of new energy sources are still facing technical bottlenecks. How to efficiently and stably connect them to the power grid and maximize their benefits is one of the problems that need to be solved in the current energy transformation process. Traditional generators can better resist short-term power grid fluctuations due to their large mechanical inertia. Even when disturbed, their output power and power grid status change relatively slowly. However, due to the widespread application of power electronic converters, new energy generators have faster system dynamic response speed and reduced inertia. Due to their low inertia characteristics, when facing power grid disturbances, system state variables such as voltage and current are prone to large fluctuations. At the same time, the acceleration of dynamic response speed also significantly enhances the sensitivity of the power system to short-term disturbances, thereby increasing the risk of system instability. All of the above not only affects the stability of the generator itself, but also extends to the entire power system through coupling effects, resulting in voltage or frequency fluctuations in the power system. For a power system containing a large number of new energy generators, the transient stability of the system is particularly fragile. Any local disturbance may spread rapidly through the nonlinear coupling effect of the power grid and affect the overall stability of the system.

[0004] At present, there are the following problems in the stability analysis of power systems: First, new energy generators have significant characteristics such as high order, strong nonlinearity, low inertia, weak damping, strong coupling, and multi-time scale interaction, which makes it very easy for the state variables of the system to change significantly when disturbed, and the transient stability becomes extremely fragile; second, with the continuous increase in the installed capacity of new energy, the power grid is bound to become weaker and weaker. In the case of a weak power grid, the stability problem caused by the interaction between the grid-connected converter and the power grid is more prominent. Moreover, the complexity of the control system of the grid-connected converter itself and the interactive coupling between different control links also make it difficult to analyze the mechanism of instability of the grid-connected converter system, which seriously restricts the safe and stable operation of the new energy power system, especially in a complex environment.

[0005] As the construction of my country's new power system is advancing in an orderly manner, new energy units with grid-connected inverters as grid interfaces will gradually become the main body of the power system. However, due to the late start of transient stability research on new energy units in China, although certain research results have been achieved, there is still a lack of rapid transient stability assessment methods and assessment indicators for grid-connected systems of new energy units, resulting in transient stability problems such as unclear system instability mechanisms and unclear fault morphology evolution laws. Summary of the invention

[0006] The main purpose of this application is to provide a data analysis method and device for the spatial state expansion of wind turbines, so as to solve the problem that the prior art lacks a rapid transient stability assessment method and assessment index for new energy unit grid-connected systems, resulting in unclear system instability mechanisms and unclear fault morphology evolution laws.

[0007] In order to achieve the above objectives, this application provides the following technical solutions: A data analysis method for spatial state expansion of a wind turbine generator set, wherein the spatial state expansion of the wind turbine generator set is based on a wind turbine generator set grid-connected system established in a wind power project, and the data analysis method comprises: Step S1, constructing a full-order model based on the topological structure of the wind turbine grid-connected system; Step S2, reducing the order of the full-order model to obtain a reduced-order model; Step S3, simulating grid voltage drop and heavy load switching based on the full-order model and the reduced-order model, respectively, to obtain a full-order output state of the full-order model and a reduced-order output state of the reduced-order model; Step S4, determining whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold, if so, executing step S5; Step S5, defining the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model; Step S6, defining the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; Step S7, defining the Lyapunov function as a sufficient condition for a subset of the attraction domain of the wind turbine grid-connected system, wherein the sufficient condition includes the stability domain; Step S8, iterating the stable domain with the sufficient condition as a constraint through an optimization algorithm until the stable domain reaches a maximum value; Step S9, obtaining a Lyapunov function that matches the maximum value as the maximum estimated attraction domain of the wind turbine grid-connected system.

[0008] As a further improvement of the present application, step S1, constructing a full-order model based on the topological structure of the wind turbine grid-connected system, includes: Step S11, defining the state variables of the wind turbine grid-connected system according to formula (1): (1); in, is the deviation angle between the current loop and the phase-locked loop, is the output frequency difference between the phase-locked and unlocked phase-locked loops, and They are the dq axis components of the current loop output before and after the control delay, is the dq axis component of the filter output current, is the dq axis component of the current loop output voltage, is the dq axis component of the grid current, and the subscript T is the transposed matrix; Step S12, the state variable Converting to standard form, we obtain the full-order model (2): (2); in, is the phase-locked loop proportional coefficient, is the phase-locked loop integral coefficient, is the current loop proportionality coefficient, is the current loop integral coefficient, is the d-axis component of the current loop setting value, is the q-axis component of the current loop set value, is the first-order differential of the d-axis component of the current loop set value, is the first-order differential of the q-axis component of the current loop set value, is the instantaneous angular frequency of the phase-locked loop, is the filter inductance of the LC filter, is the voltage feed-forward coefficient, is the total delay of the controller, is the filter capacitor of the LC filter, is the equivalent resistance between the grid connection point and the grid, is the grid voltage, is the equivalent inductance between the grid connection point and the grid.

[0009] As a further improvement of the present application, step S2, reducing the order of the full-order model to obtain a reduced-order model, includes: Step S21: to Defined as non-critical factors and ignored; Step S22, defining the grid connection point voltage and grid connection point current of the wind turbine grid connection system according to formula (3): (3); in, is the grid connection point voltage, is the d-axis component of the grid-connected point voltage, is the q-axis component of the grid-connected point voltage, is the proportionality coefficient and No need to seek, is the phase angle of the phase-locked loop, is the grid-connected point current, is the d-axis component of the grid-connected point current, is the q-axis component of the grid-connected point current, is the natural logarithm; Step S23, defining the grid voltage of the wind turbine grid-connected system according to formula (4): (4); in, is the grid voltage, is the phase angle of the power grid; Step S24, defining the line equivalent impedance of the wind turbine grid-connected system according to equation (5): (5); in, is the equivalent impedance of the line; Step S25, based on the grid connection point voltage of the topological structure and the grid voltage, equation (4) and equation (5) are combined to obtain equation (6): (6); Step S26: multiply both sides of the equation by , to eliminate and get and Based on the deviation angle as well as The expression (7) is: (7); Step S27, substitute equation (7) into and , and the reduced-order model (8) is obtained: (8); in, is the equilibrium point of the topological structure, is the grid angular frequency.

[0010] As a further improvement of the present application, step S5, defining the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model, includes: Step S51, defining the system function (9) of the wind turbine grid-connected system according to the reduced-order model: (9); in, For the system function Branches, For the system function Branches; Step S52, converting the system function into the Jacobian matrix (10): (10).

[0011] As a further improvement of the present application, step S6, defining the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory, includes: Step S61, defining the Lyapunov function (11) based on the Lyapunov stability theory: (11); in, is the Lyapunov function, is the definition domain of the stability domain; Step S62: If there is a Lyapunov function that satisfies the Jacobian matrix, the stability region is defined according to equation (12): (12).

[0012] As a further improvement of the present application, step S7 defines the Lyapunov function as a sufficient condition for a subset of the attraction domain of the wind turbine grid-connected system, wherein the sufficient condition includes the stability domain, including: Step S71, defining the sufficient condition according to the Lyapunov stability theory according to formula (13): (13); in, is the attraction domain subset, For all state variables dimensional vector set, is the gradient operator, for The gradient of is the reduced-order model.

[0013] As a further improvement of the present application, step S8, iterating the stable domain with the sufficient condition as a constraint through an optimization algorithm until the stable domain reaches a maximum value, includes: Step S81, defining a plurality of random solutions based on the stability domain; Step S82, performing iterative regional constraints based on each random solution through the sufficient condition; Step S83, defining the optimization result of all random solutions as the maximum value of the stable domain; Step S84, initializing the position of each random solution, and updating the current position and current speed of each random solution respectively; Step S85, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update; Step S86, respectively determining whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold, if both are less than, executing step S87; Step S87, respectively determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold, if both are less than, executing step S88; Step S88: determining whether the optimal solution of the stable domain has been obtained.

[0014] In order to achieve the above objectives, this application also provides the following technical solutions: A data analysis device for spatial state expansion of a wind turbine generator set, the data analysis device is applied to the above-mentioned data analysis method, and the data analysis device comprises: A full-order model building module, used to build a full-order model based on the topological structure of the wind turbine grid-connected system; A reduced-order model building module is used to reduce the order of the full-order model to obtain a reduced-order model; A model virtual scene simulation module simulates grid voltage drop and heavy load switching based on the full-order model and the reduced-order model, respectively, to obtain a full-order output state of the full-order model and a reduced-order output state of the reduced-order model; A reduced-order model accuracy judgment module, used to judge whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold; A system Jacobian matrix definition module, used to define the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model based on whether the data similarity is greater than or equal to a preset similarity threshold; A system stability domain definition module, used for defining the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; An attraction domain subset definition module, used to define the sufficient condition that the Lyapunov function is an attraction domain subset of the wind turbine grid-connected system, wherein the sufficient condition includes the stability domain; A stable domain iteration module, used for iterating the stable domain by using an optimization algorithm with the sufficient condition as a constraint until the stable domain reaches a maximum value; The maximum estimated attraction domain acquisition module is used to acquire the Lyapunov function matching the maximum value, which is the maximum estimated attraction domain of the wind turbine grid-connected system.

[0015] In order to achieve the above objectives, this application also provides the following technical solutions: An electronic device comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the data analysis method as described above is implemented.

[0016] In order to achieve the above objectives, this application also provides the following technical solutions: A storage medium stores program instructions, which can implement the above-mentioned data analysis method when executed by a processor.

[0017] The present application constructs a full-order model based on the topological structure of the wind turbine grid-connected system; reduces the order of the full-order model to obtain a reduced-order model; simulates grid voltage drop and large load switching based on the full-order model and the reduced-order model, respectively, to obtain the full-order output state of the full-order model and the reduced-order output state of the reduced-order model; determines whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold, and if so, defines the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model; defines the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; defines the Lyapunov function as a sufficient condition for a subset of the attraction domain of the wind turbine grid-connected system, and the sufficient condition includes the stability domain; iterates the stability domain with the sufficient condition as a constraint through an optimization algorithm until the stability domain reaches a maximum value; and obtains the Lyapunov function that matches the maximum value, which is the maximum estimated attraction domain of the wind turbine grid-connected system. This application lists the state equation of the unit for the unit state quantity, introduces nonlinear time-varying terms of key parameters such as filter inductance, capacitance, line impedance, grid impedance, etc., adds model error terms introduced by unmodeled parts such as dead zone, power tube internal resistance and voltage drop, parasitic parameters, etc., considers random disturbance terms such as state sampling error and external interference, and adopts state space modeling method to establish extended state space models of multiple types of new energy units; explores the influence of different state quantities on new energy units based on singular perturbation theory and modal analysis method, ignores the influence of some non-critical states and control links, and establishes a reduced-order model of new energy units with rapidity and accuracy in balanced transient stability analysis; and then expands the state space based on the grid-connected system of new energy units established in the project The model, combined with Lyapunov stability theory, reduces the system transient stability analysis problem to the system maximum attraction domain estimation problem, constructs an intelligent model for obtaining the system maximum attraction domain, and finally derives the system maximum estimated attraction domain, which provides accurate data support for the subsequent exploration of the mapping relationship between the maximum estimated attraction domain and system parameters and state response characteristics under typical working conditions. It also provides accurate data support for revealing the instability mechanism of the system in a typical transient process and proposing a multi-parameter collaborative setting method that can ensure the stable operation of the unit. In addition, this application reduces the order of the full-order model, making it equally applicable in complex environments, and the accuracy of subsequent calculations can be guaranteed without obtaining all high-order data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a circuit topology of a wind turbine grid-connected system according to an embodiment of a data analysis method for spatial state expansion of a wind turbine in the present application; Figure 2 A schematic diagram of the steps of an embodiment of a data analysis method for spatial state expansion of a wind turbine generator system according to the present application; Figure 3When the grid voltage drops to 0.5 pu, the data analysis method for the spatial state expansion of the wind turbine generator system according to an embodiment of the present application Waveform comparison chart; Figure 4 When the grid voltage drops to 0.5 pu, the data analysis method for the spatial state expansion of the wind turbine generator system according to an embodiment of the present application Waveform comparison chart; Figure 5 When the grid voltage drops to 0.4 pu, the data analysis method for the spatial state expansion of the wind turbine generator system according to an embodiment of the present application Waveform comparison chart; Figure 6 When the grid voltage drops to 0.4 pu, the data analysis method for the spatial state expansion of the wind turbine generator system according to an embodiment of the present application Waveform comparison chart; Figure 7 The present invention is directed to an embodiment of a data analysis method for the spatial state expansion of a wind turbine generator set, wherein the method switches from no-load to half-load. Waveform comparison chart; Figure 8 The present invention is directed to an embodiment of a data analysis method for the spatial state expansion of a wind turbine generator set, wherein the method switches from no-load to half-load. Waveform comparison chart; Fig. 9 The present invention is directed to an embodiment of a data analysis method for the spatial state expansion of a wind turbine generator set, wherein the method switches from no-load to full-load. Waveform comparison chart; Fig.10 The present invention is directed to an embodiment of a data analysis method for the spatial state expansion of a wind turbine generator set, wherein the method switches from no-load to full-load. Waveform comparison chart; Fig.11 The present invention is a data analysis method for the spatial state expansion of a wind turbine generator set. The voltage drops from full load to overload at the same time. Waveform comparison chart; Fig.12 The present invention is a data analysis method for the spatial state expansion of a wind turbine generator set. The voltage drops from full load to overload at the same time. Waveform comparison chart; Fig.13 A phase-locked loop control block diagram of an embodiment of a data analysis method for spatial state expansion of a wind turbine generator system according to the present application; Fig.14 This is a phase-locked loop equivalent control block diagram of an embodiment of the data analysis method for spatial state expansion of a wind turbine generator system in the present application; Fig.15This is a functional module diagram of an embodiment of a data analysis device for spatial state expansion of a wind turbine generator system according to the present application; Fig.16 This is a schematic diagram of the structure of an embodiment of the electronic device of the present application; Fig.17 This is a schematic diagram of the structure of an embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0021] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] like Figure 1 As shown, a data analysis method for spatial state expansion of wind turbines is provided, wherein the spatial state expansion of wind turbines is based on a wind turbine grid-connected system established in a wind power project.

[0023] Preferably, the control strategy of common new energy grid-connected inverters in normal operation is voltage and current dual-loop control: the voltage outer loop indirectly controls the DC side voltage by adjusting the reference signal of the current inner loop, thereby maintaining the DC voltage stability; the current inner loop quickly adjusts the inverter output current to ensure that the inverter output current can accurately track the given reference signal. However, when a serious fault occurs in the power grid, the grid-connected inverter generally enters the current limiting control mode, at which time the voltage outer loop will be disconnected and only the current inner loop will be retained. Since the analysis of system transient stability mainly focuses on power grid fault conditions, the modeling in this article does not consider the outer loop control, but only focuses on the current inner loop and phase-locked loop.

[0024] See also Figure 1 ,in is the DC side voltage of the inverter, is the inverter port voltage, is the grid voltage, is the grid voltage, is the grid-connected current, is the inductor current, and are the filter inductance and filter capacitance of the LC filter, and is the equivalent resistance and equivalent inductance between the grid connection point and the grid, is the phase angle of the phase-locked loop, [ , ]and[ , ] are the dq axis components of the grid connection point voltage and current respectively, [ , ] are the dq axis components of the current loop setting value, is the voltage feed-forward coefficient, [ , ]and[ , ] are the dq axis components of the current loop output before and after considering the control delay, is the dq axis component of the filter output current, is the dq axis component of the current loop output voltage, is the dq axis component of the grid current, and the subscript T is the transposed matrix.

[0025] Specifically, see Figure 2 , the data analysis method comprises the following steps: Step S1, constructing a full-order model based on the topological structure of the wind turbine grid-connected system.

[0026] Step S2, reducing the order of the full-order model to obtain a reduced-order model.

[0027] Step S3, simulating grid voltage drop and heavy load switching based on the full-order model and the reduced-order model, respectively, to obtain the full-order output state of the full-order model and the reduced-order output state of the reduced-order model.

[0028] Preferably, the working condition of the grid voltage drop is simulated as follows: Working condition 1: The grid voltage drops from 1p.u. to 0.5pu. Figure 3 and Figure 4 As shown in the figure, when the grid voltage drops to 0.5pu, the output state of the reduced-order model is and It is basically consistent with the state of the full-order model.

[0029] Working condition 2: The grid voltage drops from 1p.u. to 0.4pu. Figure 5 and Figure 6 As shown in Figure 1, when the grid voltage drops to 0.4 pu, the system diverges, and the output states x1 and x2 of the reduced-order model are still basically consistent with the states of the full-order model.

[0030] Preferably, the working condition simulation of large load switching is as follows: Working condition 1: Switch from no load (0p.u.) to half load (0.5pu). Figure 7 and Figure 8 It can be seen that when the system switches from no-load to half-load, the output states x1 and x2 of the reduced-order model are basically consistent with the states of the average model.

[0031] Working condition 2: Switch from no-load (0p.u.) to full-load (1.0pu). Fig. 9 and Fig.10 It can be seen that when the system switches from no-load to full-load, the output states x1 and x2 of the reduced-order model are basically consistent with the states of the average model, indicating that the reduced-order model can simulate the full-order model well.

[0032] Condition 3: Switching from full load (1 pu) to overload (1.5 pu), while the voltage drops to 0.5 pu. Fig.11 and Fig.12 It can be seen that when the system switches from full load to overload and the voltage drops to 0.5 pu, the output states x1 and x2 of the reduced-order model are basically consistent with the states of the average model.

[0033] In summary, the reduced-order model can simulate the full-order model well in a variety of working conditions, ensuring the accuracy of subsequent data.

[0034] Preferably, ‌p.u. is the abbreviation of per unit value, expressed as per unit, which is not a specific physical unit, but a dimensionless relative value. ‌In power systems, pu is widely used to represent various physical quantities and parameters in circuit calculations. Specifically, pu is a dimensionless value obtained by dividing the actual value by the corresponding reference value. For example, if a physical quantity A has its corresponding reference value AB, then the per unit value A of A can be calculated by the formula A = A / AB. In the analysis and calculation of power systems, the use of the per unit system (i.e., pu) can intuitively and quickly judge the correctness of system component parameters and state variables, and can greatly simplify the calculation process. For example, in a 220V circuit, 1p.u. is equal to 220V, and 0.1pu is equal to 22V.

[0035] Step S4, determining whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold, if so, executing step S5.

[0036] Preferably, the data similarity can be measured by matrix distance, Minkowski distance, or Spearman rank correlation coefficient.

[0037] Preferably, the present embodiment preferably uses the Spearman rank correlation coefficient, specifically: ,in, is the Spearman Rank Correlation coefficient, is the number of pairs of data, For the The difference in the level of data.

[0038] Preferably, since the similarity judgment can be based on Figures 3 to 12 The data is used for judgment. Since the simulation effect of the reduced-order model is good, the preset similarity threshold can be set to 98% to 99%.

[0039] It should be noted that the formulas in the above additional content are for principle explanation only, and the symbolic meanings of the formulas are not interchangeable with other formulas.

[0040] Step S5, defining the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model.

[0041] Preferably, the Jacobian matrix is ​​a matrix of first-order partial derivatives arranged in a certain way. Suppose there is a vector-valued function ‌y=f(x)‌, where ‌x‌ is the input vector and ‌y‌ is the output vector. The Jacobian matrix ‌J‌ is an m×n matrix, each element of which is the partial derivative of ‌fi‌ with respect to ‌xj‌.

[0042] ‌Linear Approximation‌: The Jacobian matrix represents the optimal linear approximation of a differentiable equation to a given point. It describes the local linear transformation characteristics of a function near a certain point and is a linear approximation of the local function to the input change.

[0043] ‌Determinant‌: When the Jacobian matrix is ​​square (i.e. m=n), we can take its determinant, called the Jacobian determinant. The Jacobian determinant provides important information about how a function behaves as it approaches a given point. For example, if the Jacobian determinant of a continuously differentiable function at a point is not zero, then it has an inverse function near that point.

[0044] ‌‌Stationary point‌: When all elements of the Jacobian matrix at a certain point are zero, it means that all first-order partial derivatives of that point are zero, indicating that the point is a stationary point (critical point) of the function.

[0045] Step S6, defining the Jacobian matrix to satisfy the stability domain of the Lyapunov function according to the Lyapunov stability theory.

[0046] Preferably, Lyapunov stability theory mainly refers to Lyapunov's second method, also known as Lyapunov's direct method. Lyapunov's second method can be used for systems of any order. This method can be used to directly determine stability without solving the system state equation. For nonlinear systems and time-varying systems, solving the state equation is often very difficult, so Lyapunov's second method shows great superiority.

[0047] Preferably, Lyapunov stability theory has three main theorems: ① Uniform asymptotic stability theorem: For a system described by a state equation, if there exists a Lyapunov function V(x, t) that satisfies the conditions: V(x, t) is positive definite and has an infinitesimal upper bound, and is negative definite, then the origin equilibrium state xe=0 is uniformly asymptotically stable. In many cases, it is often difficult to find a Lyapunov function that satisfies the negative definite condition. For this case, the negative definite condition in the theorem can be equivalently relaxed: it is semi-negative definite, and is not always zero along any disturbed motion trajectory.

[0048] ② Large-scale uniform asymptotic stability theorem: If the system's equilibrium state xe=0 is uniformly asymptotically stable, and the Lyapunov function V(x, t) tends to infinity with , where x1,…,xn are the components of state x, then the equilibrium state at the origin is uniformly asymptotically stable over a large scale.

[0049] ③ Instability theorem: If for xe=0, a scalar function W(x, t) can be found from the system described by the state equation so that W(0, t)=0 holds and is positive definite in the region satisfying W(x, t)≥0, then the equilibrium state at the origin is unstable.

[0050] Preferably, Lyapunov's second method is used to estimate the rapidity of transient response. For systems judged to be asymptotically stable by Lyapunov's second method, an estimate of the rapidity of system transient response can also be obtained by the Lyapunov function. represents the ratio of positive definite V(x, t) and negative definite, which must be positive. Then take ηmin to represent the minimum value of η, then an estimate of the decay speed of the Lyapunov function can be obtained by the relationship V(x, t)≤V(x0, t0)exp(-ηmin(t-t0)), where V(x0, t0) is the function value at the initial time t0 and the initial state x0. V(x, t) can be expanded into a first-order number with the x2 term as the first term, so ηmin can also be used as an indicator to estimate the decay speed of the transient response. A smaller ηmin value corresponds to a faster transient response. For nonlinear systems, the value of ηmin can be easily determined only when the form of V(x, t) is relatively simple. For linear time-invariant systems, ηmin is equal to the eigenvalues ​​of the matrix QP-1, which is generally easy to determine, where P-1 is the inverse matrix of the solution matrix P of the Lyapunov equation.

[0051] It should be noted that the symbols in the above additional content are for principle explanation and their meanings are not interchangeable with other formulas.

[0052] Step S7, defining the Lyapunov function as a sufficient condition for the subset of the attraction domain of the wind turbine grid-connected system, wherein the sufficient condition includes a stable domain.

[0053] Preferably, the stability domain refers to the area or range where the system can maintain stable operation under certain conditions, describing the range of parameters within which the system can maintain its stability and reliability. The attraction domain is the area where all points starting from this area will eventually converge to a certain equilibrium point. The trajectory of these points will tend to infinity over time and eventually converge to the equilibrium point.

[0054] Preferably, the attraction domain is a region where all points starting from the region will eventually converge to a certain equilibrium point, and the trajectories of these points will tend to infinity over time and eventually converge to the equilibrium point.

[0055] Preferably, the largest subset of the attraction domain is the attraction domain itself.

[0056] Step S8, iterating the stable domain with sufficient conditions as constraints through an optimization algorithm until the stable domain reaches a maximum value.

[0057] Preferably, this embodiment can also find the optimal solution through the LMI optimization algorithm.

[0058] Step S9, obtaining a Lyapunov function that matches the maximum value is the maximum estimated attraction domain of the wind turbine grid-connected system.

[0059] Preferably, this embodiment ignores the influence of some non-critical states and control links, and establishes a reduced-order model of new energy units that balances the rapidity and accuracy of transient stability analysis; then based on the expanded state space model of the new energy unit grid-connected system established in the project, combined with Lyapunov stability theory, the system transient stability analysis problem is reduced to the problem of estimating the maximum attraction domain of the system, and an intelligent model for obtaining the maximum attraction domain of the system is constructed. Finally, the system maximum estimated attraction domain is derived, and the system maximum estimated attraction domain can reflect the transient stability of the system, that is, the spatial state of the system.

[0060] Further, in step S1, a full-order model is constructed based on the topological structure of the wind turbine grid-connected system, including: Step S11, define the state variables of the wind turbine grid-connected system according to formula (1): (1).

[0061] in, is the deviation angle between the current loop and the phase-locked loop, is the output frequency difference between the phase-locked and unlocked phase-locked loop, and They are the dq axis components of the current loop output before and after the control delay, is the dq axis component of the filter output current, is the dq axis component of the current loop output voltage, is the dq axis component of the grid current, and the subscript T is the transposed matrix.

[0062] Preferably, according to Figure 1 The topological structure of the system shows that the phase-locked loop equation is: .

[0063] The current loop equation is: .

[0064] in,[ , ] is the dq axis component of the grid current.

[0065] Due to the control delay in the system, the specific , substituting into the system we get: ,in To control delay.

[0066] Depend on Figure 1 The topological structure of the system can obtain the voltage and current equations of the system as follows: .

[0067] in,[ , ] is the first-order differential of the dq-axis component of the filter output current.

[0068] Step S12: Set the state variable Converting to standard form, we obtain the full-order model (2): (2).

[0069] in, is the phase-locked loop proportional coefficient, is the phase-locked loop integral coefficient, is the current loop proportionality coefficient, is the current loop integral coefficient, is the d-axis component of the current loop setting value, is the q-axis component of the current loop set value, is the first-order differential of the d-axis component of the current loop set value, is the first-order differential of the q-axis component of the current loop set value, is the instantaneous angular frequency of the phase-locked loop, is the filter inductance of the LC filter, is the voltage feed-forward coefficient, is the total delay of the controller, is the filter capacitor of the LC filter, is the equivalent resistance between the grid connection point and the grid, is the grid voltage, is the equivalent inductance between the grid connection point and the grid.

[0070] Preferably, since the order of the full-order model is very high and there are nonlinear terms introduced by phase-locked control and the transformation of the two dq coordinate systems. In addition, due to the deviation angle between the current loop and the phase-locked loop 、Filter inductor and grid current The relationship between the full-order model and the model results in a complex coupling relationship. Therefore, the full-order model exhibits high-order, nonlinear and strongly coupled characteristics. In order to facilitate analysis and reduce the burden of computer computing power, the full-order model can be reduced in order.

[0071] Further, in step S2, the full-order model is subjected to order reduction processing to obtain a reduced-order model, including: Step S21: to Defined as non-critical factor and ignored.

[0072] Preferably, the order reduction process in step S2 is based on the following assumptions: ① Ignore non-critical influencing factors, such as LC filters, control delays, and feedforward.

[0073] ②The phase-locked loop bandwidth is much smaller than the current loop bandwidth, so assume [ , ]Approximately real-time tracking of current loop setpoints[ , ].

[0074] Step S22, defining the grid connection point voltage and grid connection point current of the wind turbine grid connection system according to formula (3): (3).

[0075] in, is the grid voltage, is the d-axis component of the grid-connected point voltage, is the q-axis component of the grid-connected point voltage, is the proportionality coefficient and No need to seek, is the phase angle of the phase-locked loop, is the grid-connected current, is the d-axis component of the grid-connected point current, is the q-axis component of the grid-connected point current, is the natural logarithm.

[0076] Step S23, define the grid voltage of the wind turbine grid-connected system according to formula (4): (4).

[0077] in, is the grid voltage, is the phase angle of the power grid.

[0078] Step S24, define the line equivalent impedance of the wind turbine grid-connected system according to equation (5): (5).

[0079] in, is the equivalent impedance of the line.

[0080] Step S25, based on the grid connection point voltage of the topological structure and the grid voltage, equation (4) and equation (5) are combined to obtain equation (6): (6).

[0081] Step S26: multiply both sides of the equation by , to eliminate and get and Based on the deviation angle as well as The expression (7): (7).

[0082] Preferably, formula (6) multiplies both sides of the equation by get: Step S27, substitute equation (7) into and , and the reduced-order model (8) is obtained: (8).

[0083] in, is the equilibrium point of the topological structure, is the grid angular frequency.

[0084] Preferably, see Figure 3 and Figure 4 ,according to Figure 3 From the phase-locked loop control block diagram, we can know that , are the proportional and integral parameters of the phase-locked loop PI controller, is the phase-locked loop angular frequency With grid angular frequency The difference between Figure 4 The equivalent control block diagram of the phase-locked loop.

[0085] Preferably, based on Figure 4 Take the state variable as [ , ]=[ , ],available: , substitute into formula (7) and simplify to get: .

[0086] From the above formula, we can see that the equilibrium point of the system is: .

[0087] Move the system to the equilibrium point for analysis, , and we get formula (7).

[0088] Further, step S5, defining the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model, includes: Step S51, defining the system function (9) of the wind turbine grid-connected system according to the reduced-order model: (9).

[0089] in, For system functions Branches, For system functions Branch.

[0090] Step S52, convert the system function into a Jacobian matrix (10): (10).

[0091] Preferably, the Jacobian matrix of the system is , Substituting equation (7) into matrix A, we can get equation (10).

[0092] Further, step S6, according to Lyapunov stability theory, defines the Jacobian matrix to satisfy the stability domain of the Lyapunov function, including: Step S61, define the Lyapunov function (11) based on the Lyapunov stability theory: (11).

[0093] in, is the Lyapunov function, is the domain of the stability domain.

[0094] Step S62: If there is a Lyapunov function that satisfies the Jacobian matrix, the stability region is defined according to equation (12): (12).

[0095] because Determines the Lyapunov function , which in turn determines the size of the estimated stability domain. Therefore, the stability analysis problem is transformed into how to choose the appropriate Make the estimated stability region as large as possible.

[0096] Further, in step S7, the Lyapunov function is defined as a sufficient condition for the subset of the attraction domain of the wind turbine grid-connected system, and the sufficient condition includes a stable domain, including: Step S71, define sufficient conditions according to Lyapunov stability theory according to formula (13): (13).

[0097] in, To attract a subset of domains, For all state variables dimensional vector set, is the gradient operator, for The gradient of is a reduced-order model.

[0098] Further, step S8, iterating the stable domain with sufficient conditions as constraints through an optimization algorithm until the stable domain reaches a maximum value, including: Step S81, defining several random solutions based on the stable domain.

[0099] Preferably, all random solutions can be defined according to the following formula: .

[0100] in, is the set of all random solutions, For each random solution, is the label of the random solution, is the number of all random solutions; is the set of velocities of all random solutions, are the speeds of each random solution respectively.

[0101] Step S82, iterating regional constraints based on each random solution through sufficient conditions.

[0102] Step S83, defining the optimization result of all random solutions as the maximum value reached in the stable domain.

[0103] Step S84, initializing the position of each random solution, and updating the current position and current speed of each random solution respectively.

[0104] Preferably, the current position and current speed can be updated respectively based on the same random solution according to the following formula: .

[0105] in, For the The random solution is The speed of the step, For the The random solution is The speed inertia of the step, is the inertia coefficient, For the The self-perception representation of a random solution, For the social cognitive representation of a random solution; and are learning factors, for A random number, For the The individual optimal solution obtained by random solutions is For the The global optimal solution obtained by random solutions is For the Step 1 A random solution, For the Step 1 A random solution.

[0106] Preferably, The value range is , preferably ; The value range is , preferably .

[0107] Step S85, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update.

[0108] Preferably, the inertia coefficient may be linearly decreased once for each update step according to the following formula: .

[0109] in, For the The random solution is The inertia coefficient after step optimization, is the initial inertia coefficient, is the current update step number, is the maximum number of update steps.

[0110] It should be noted that the formulas in the above additional content are for principle explanation only, and the meanings of the symbols are not interchangeable with other formulas.

[0111] Preferably, the initial inertia coefficient is generally set to 0.5, and the maximum update step number is generally set according to actual needs, and in this embodiment can be set to 10,000 times.

[0112] Preferably, the values ​​of the first preset adaptation threshold and the second preset adaptation threshold need to be adjusted according to the specific problem, and generally need to be adjusted according to the calculation results. If the adaptation threshold is set too small, the algorithm may stop prematurely and fail to obtain the optimal solution; if the adaptation threshold is set too large, the algorithm may be over-updated, wasting computing resources.

[0113] Preferably, the adaptation threshold may also be evaluated by one of the Griewank function, the Rastrigin function, the Schaffer function, the Ackley function, and the Rosenbrock function.

[0114] Step S86, respectively determine whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold. If both are less than, execute step S87.

[0115] Step S87, determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold. If both are less than, executing step S88.

[0116] Step S88, determining whether the optimal solution of the stable domain has been obtained.

[0117] This embodiment constructs a full-order model based on the topological structure of the wind turbine grid-connected system; reduces the order of the full-order model to obtain a reduced-order model; simulates grid voltage drop and large load switching based on the full-order model and the reduced-order model, respectively, to obtain the full-order output state of the full-order model and the reduced-order output state of the reduced-order model; determines whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold, and if so, defines the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model; defines the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; defines the Lyapunov function as a sufficient condition for a subset of the attraction domain of the wind turbine grid-connected system, and the sufficient condition includes the stability domain; iterates the stability domain with the sufficient condition as a constraint through an optimization algorithm until the stability domain reaches a maximum value; and obtains the Lyapunov function that matches the maximum value, which is the maximum estimated attraction domain of the wind turbine grid-connected system. This embodiment writes the state equation of the unit for the unit state quantity, introduces nonlinear time-varying terms of key parameters such as filter inductance, capacitance, line impedance, and grid impedance, adds model error terms introduced by unmodeled parts such as dead zone, power tube internal resistance and voltage drop, and parasitic parameters, considers random disturbance terms such as state sampling error and external interference, and adopts state space modeling method to establish extended state space models of multiple types of new energy units; explores the influence of different state quantities on new energy units based on singular perturbation theory and modal analysis method, ignores the influence of some non-critical states and control links, and establishes a reduced-order model of new energy units with rapidity and accuracy in balanced transient stability analysis; and then expands the state space based on the grid-connected system of new energy units established in the project The model is combined with Lyapunov stability theory, and the problem of transient stability analysis of the system is reduced to the problem of estimating the maximum attraction domain of the system. An intelligent model for obtaining the maximum attraction domain of the system is constructed, and finally the maximum estimated attraction domain of the system is derived, which provides accurate data support for the subsequent exploration of the mapping relationship between the maximum estimated attraction domain and system parameters and state response characteristics under typical working conditions. At the same time, it also provides accurate data support for revealing the instability mechanism of the system in a typical transient process and proposing a multi-parameter collaborative setting method that can ensure the stable operation of the unit. In addition, this embodiment reduces the order of the full-order model, which makes this embodiment also applicable in complex environments, and the accuracy of subsequent calculations can be guaranteed without obtaining all high-order data.

[0118] like Figure 5 As shown, this embodiment provides an embodiment of a data analysis device for spatial state expansion of a wind turbine generator set. In this embodiment, the data analysis device is applied to the data analysis method in the above embodiment.

[0119] Specifically, the data analysis device includes a full-order model construction module 1, a reduced-order model construction module 2, a model virtual scene simulation module 3, a reduced-order model accuracy judgment module 4, a system Jacobian matrix definition module 5, a system stable domain definition module 6, an attraction domain subset definition module 7, a stable domain iteration module 8, and a maximum estimated attraction domain acquisition module 9, which are electrically connected in sequence.

[0120] Among them, the full-order model construction module 1 is used to construct a full-order model based on the topological structure of the wind turbine grid-connected system; the reduced-order model construction module 2 is used to reduce the full-order model to obtain a reduced-order model; the model virtual scene simulation module 3 simulates the grid voltage drop and large load switching based on the full-order model and the reduced-order model, respectively, to obtain the full-order output state of the full-order model and the reduced-order output state of the reduced-order model; the reduced-order model accuracy judgment module 4 is used to judge whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to the preset similarity threshold; the system Jacobian matrix definition module 5 is used to determine whether the data similarity is greater than or equal to the preset similarity threshold. The degree threshold defines the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model; the system stability domain definition module 6 is used to define the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; the attraction domain subset definition module 7 is used to define the sufficient condition that the Lyapunov function is the attraction domain subset of the wind turbine grid-connected system, and the sufficient condition includes the stability domain; the stability domain iteration module 8 is used to iterate the stability domain with the sufficient condition as the constraint through the optimization algorithm until the stability domain reaches the maximum value; the maximum estimated attraction domain acquisition module 9 is used to obtain the Lyapunov function matching the maximum value, which is the maximum estimated attraction domain of the wind turbine grid-connected system.

[0121] Furthermore, the full-order model construction module 1 specifically includes a first full-order model construction sub-module and a second full-order model construction sub-module which are electrically connected in sequence; the second full-order model construction sub-module is electrically connected to the reduced-order model construction module 2 .

[0122] Among them, the first full-order model building submodule is used to define the state variables of the wind turbine grid-connected system according to formula (1): (1).

[0123] in, is the deviation angle between the current loop and the phase-locked loop, is the output frequency difference between the phase-locked and unlocked phase-locked loop, and They are the dq axis components of the current loop output before and after the control delay, is the dq axis component of the filter output current, is the dq axis component of the current loop output voltage, is the dq axis component of the grid current, and the subscript T is the transposed matrix.

[0124] The second full-order model building submodule is used to convert the state variables Converting to standard form, we obtain the full-order model (2): (2).

[0125] in, is the phase-locked loop proportional coefficient, is the phase-locked loop integral coefficient, is the current loop proportionality coefficient, is the current loop integral coefficient, is the d-axis component of the current loop setting value, is the q-axis component of the current loop set value, is the first-order differential of the d-axis component of the current loop set value, is the first-order differential of the q-axis component of the current loop set value, is the instantaneous angular frequency of the phase-locked loop, is the filter inductance of the LC filter, is the voltage feed-forward coefficient, is the total delay of the controller, is the filter capacitor of the LC filter, is the equivalent resistance between the grid connection point and the grid, is the grid voltage, is the equivalent inductance between the grid connection point and the grid.

[0126] Furthermore, the reduced-order model construction module 2 specifically includes a first reduced-order model construction sub-module, a second reduced-order model construction sub-module, a third reduced-order model construction sub-module, a fourth reduced-order model construction sub-module, a fifth reduced-order model construction sub-module, a sixth reduced-order model construction sub-module, and a seventh reduced-order model construction sub-module, which are electrically connected in sequence; the first reduced-order model construction sub-module is electrically connected to the second full-order model construction sub-module.

[0127] Among them, the first reduced-order model construction submodule is used to convert to Defined as non-critical factor and ignored.

[0128] The second reduced-order model building submodule is used to define the grid connection point voltage and grid connection point current of the wind turbine grid-connected system according to equation (3): (3).

[0129] in, is the grid voltage, is the d-axis component of the grid-connected point voltage, is the q-axis component of the grid-connected point voltage, is the proportionality coefficient and No need to seek, is the phase angle of the phase-locked loop, is the grid-connected current, is the d-axis component of the grid-connected point current, is the q-axis component of the grid-connected point current, is the natural logarithm.

[0130] The third reduced-order model building submodule is used to define the grid voltage of the wind turbine grid-connected system according to equation (4): (4).

[0131] in, is the grid voltage, is the phase angle of the power grid.

[0132] The fourth reduced-order model building submodule is used to define the line equivalent impedance of the wind turbine grid-connected system according to equation (5): (5).

[0133] in, is the equivalent impedance of the line.

[0134] The fifth reduced-order model building submodule is used to combine the grid connection point voltage and the grid voltage based on the topological structure to obtain equation (6): (6).

[0135] The sixth reduced-order model building submodule is used to multiply both sides of the equation by , to eliminate and get and Based on the deviation angle as well as The expression (7): (7).

[0136] The seventh reduced-order model building submodule is used to substitute equation (7) into and , and the reduced-order model (8) is obtained: (8).

[0137] in, is the equilibrium point of the topological structure, is the grid angular frequency.

[0138] Furthermore, the system Jacobian matrix definition module 5 specifically includes a first system Jacobian matrix definition submodule and a second system Jacobian matrix definition submodule which are electrically connected in sequence; the first system Jacobian matrix definition submodule is electrically connected to the reduced-order model accuracy judgment module 4, and the second system Jacobian matrix definition submodule is electrically connected to the system stability domain definition module 6.

[0139] The first system Jacobian matrix definition submodule is used to define the system function of the wind turbine grid-connected system according to the reduced-order model (9): (9).

[0140] in, For system functions Branches, For system functions Branch.

[0141] The second system Jacobian matrix definition submodule converts the system function into a Jacobian matrix (10): (10).

[0142] Furthermore, the system stability domain definition module 6 specifically includes a first system stability domain definition submodule and a second system stability domain definition submodule which are electrically connected in sequence; the first system stability domain definition submodule is electrically connected to the second system Jacobian matrix definition submodule, and the second system stability domain definition submodule is electrically connected to the attraction domain subset definition module 7.

[0143] Among them, the first system stability domain definition submodule is used to define the Lyapunov function (11) based on the Lyapunov stability theory: (11).

[0144] in, is the Lyapunov function, is the domain of the stability domain.

[0145] The second system stability region definition submodule is used to define the stability region according to formula (12) if there is a Lyapunov function that satisfies the Jacobian matrix: (12).

[0146] Furthermore, the attraction domain subset definition module 7 is specifically used to define the sufficient condition according to the Lyapunov stability theory according to formula (13): (13).

[0147] in, To attract a subset of domains, For all state variables dimensional vector set, is the gradient operator, for The gradient of is a reduced-order model.

[0148] Furthermore, the stable domain iteration module 8 specifically includes a first stable domain iteration submodule, a second stable domain iteration submodule, a third stable domain iteration submodule, a fourth stable domain iteration submodule, a fifth stable domain iteration submodule, a sixth stable domain iteration submodule, a seventh stable domain iteration submodule, and an eighth stable domain iteration submodule, which are electrically connected in sequence; the first stable domain iteration submodule is electrically connected to the attraction domain subset definition module 7, and the eighth stable domain iteration submodule is electrically connected to the maximum estimated attraction domain acquisition module 9.

[0149] Among them, the first stable domain iterative submodule is used to define several random solutions based on the stable domain; the second stable domain iterative submodule is used to perform iterative region constraints based on each random solution through sufficient conditions; the third stable domain iterative submodule is used to define the optimization result of all random solutions as the maximum value reached in the stable domain; the fourth stable domain iterative submodule is used to initialize the position of each random solution, and update the current position and current speed of each random solution respectively; the fifth stable domain iterative submodule is used to obtain the individual optimal solution and the global optimal solution of each random solution based on each update respectively; the sixth stable domain iterative submodule is used to determine whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to the first preset adaptation threshold; the seventh stable domain iterative submodule is used to determine whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to the second preset adaptation threshold if both are less than; the eighth stable domain iterative submodule is used to determine that the optimal solution of the stable domain has been obtained if both are less than.

[0150] It should be noted that this embodiment is a functional module embodiment based on the above method embodiment. The optimization, expansion, limitation, example, and principle description of this embodiment can be referred to the above embodiment, and this embodiment will not be repeated.

[0151] This embodiment constructs a full-order model based on the topological structure of the wind turbine grid-connected system; reduces the order of the full-order model to obtain a reduced-order model; simulates grid voltage drop and large load switching based on the full-order model and the reduced-order model, respectively, to obtain the full-order output state of the full-order model and the reduced-order output state of the reduced-order model; determines whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold, and if so, defines the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model; defines the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; defines the Lyapunov function as a sufficient condition for a subset of the attraction domain of the wind turbine grid-connected system, and the sufficient condition includes the stability domain; iterates the stability domain with the sufficient condition as a constraint through an optimization algorithm until the stability domain reaches a maximum value; and obtains the Lyapunov function that matches the maximum value, which is the maximum estimated attraction domain of the wind turbine grid-connected system. This embodiment writes the state equation of the unit for the unit state quantity, introduces nonlinear time-varying terms of key parameters such as filter inductance, capacitance, line impedance, and grid impedance, adds model error terms introduced by unmodeled parts such as dead zone, power tube internal resistance and voltage drop, and parasitic parameters, considers random disturbance terms such as state sampling error and external interference, and adopts state space modeling method to establish extended state space models of multiple types of new energy units; explores the influence of different state quantities on new energy units based on singular perturbation theory and modal analysis method, ignores the influence of some non-critical states and control links, and establishes a reduced-order model of new energy units with rapidity and accuracy in balanced transient stability analysis; and then expands the state space based on the grid-connected system of new energy units established in the project The model is combined with Lyapunov stability theory, and the problem of transient stability analysis of the system is reduced to the problem of estimating the maximum attraction domain of the system. An intelligent model for obtaining the maximum attraction domain of the system is constructed, and finally the maximum estimated attraction domain of the system is derived, which provides accurate data support for the subsequent exploration of the mapping relationship between the maximum estimated attraction domain and system parameters and state response characteristics under typical working conditions. At the same time, it also provides accurate data support for revealing the instability mechanism of the system in a typical transient process and proposing a multi-parameter collaborative setting method that can ensure the stable operation of the unit. In addition, this embodiment reduces the order of the full-order model, which makes this embodiment also applicable in complex environments, and the accuracy of subsequent calculations can be guaranteed without obtaining all high-order data.

[0152] like Figure 6 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101 .

[0153] The memory 102 stores program instructions for implementing the data analysis method for spatial state expansion of wind turbine generator systems according to any of the above embodiments.

[0154] The processor 101 is used to execute program instructions stored in the memory 102 to perform data analysis on the spatial state expansion of the wind turbine generator system.

[0155] The processor 101 may also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip having data processing capabilities. The processor 101 may also be a general-purpose processor, a digital data processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0156] Further, Figure 7 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 11 of the embodiment of the present application stores program instructions 111 that can implement all the above methods, wherein the program instructions 111 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0157] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0158] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0159] The specific implementation methods of the present application are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the present application is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A data analysis method for spatial state expansion of a wind turbine generator set, wherein the spatial state expansion of the wind turbine generator set is based on a wind turbine generator set grid-connected system established in a wind power project, characterized in that: The data analysis method comprises: Step S1, constructing a full-order model based on the topological structure of the wind turbine grid-connected system; Step S2, reducing the order of the full-order model to obtain a reduced-order model; Step S3, simulating grid voltage drop and heavy load switching based on the full-order model and the reduced-order model, respectively, to obtain a full-order output state of the full-order model and a reduced-order output state of the reduced-order model; Step S4, determining whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold, if so, executing step S5; Step S5, defining the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model; Step S6, defining the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; Step S7, defining the Lyapunov function as a sufficient condition for a subset of the attraction domain of the wind turbine grid-connected system, wherein the sufficient condition includes the stability domain; Step S8, iterating the stable domain with the sufficient condition as a constraint through an optimization algorithm until the stable domain reaches a maximum value; Step S9, obtaining a Lyapunov function that matches the maximum value as the maximum estimated attraction domain of the wind turbine grid-connected system.

2. The data analysis method according to claim 1, characterized in that: Step S1, constructing a full-order model based on the topological structure of the wind turbine grid-connected system, including: Step S11, defining the state variables of the wind turbine grid-connected system according to formula (1): （1）； in, is the deviation angle between the current loop and the phase-locked loop, is the output frequency difference between the phase-locked and unlocked phase-locked loops, and They are the dq axis components of the current loop output before and after the control delay, is the dq axis component of the filter output current, is the dq axis component of the current loop output voltage, is the dq axis component of the grid current, and the subscript T is the transposed matrix; Step S12: the state variable Converting to standard form, we obtain the full-order model (2): （2）； in, is the phase-locked loop proportional coefficient, is the phase-locked loop integral coefficient, is the current loop proportionality coefficient, is the current loop integral coefficient, is the d-axis component of the current loop setting value, is the q-axis component of the current loop set value, is the first-order differential of the d-axis component of the current loop set value, is the first-order differential of the q-axis component of the current loop set value, is the instantaneous angular frequency of the phase-locked loop, is the filter inductance of the LC filter, is the voltage feed-forward coefficient, is the total delay of the controller, is the filter capacitor of the LC filter, is the equivalent resistance between the grid connection point and the grid, is the grid voltage, is the equivalent inductance between the grid connection point and the grid.

3. The data analysis method according to claim 2, characterized in that: Step S2, reducing the order of the full-order model to obtain a reduced-order model, including: Step S21: to Defined as non-critical factors and ignored; Step S22, defining the grid connection point voltage and grid connection point current of the wind turbine grid connection system according to formula (3): （3）； in, is the grid connection point voltage, is the d-axis component of the grid-connected point voltage, is the q-axis component of the grid-connected point voltage, is the proportionality coefficient and No need to seek, is the phase angle of the phase-locked loop, is the grid-connected point current, is the d-axis component of the grid-connected point current, is the q-axis component of the grid-connected point current, is the natural logarithm; Step S23, defining the grid voltage of the wind turbine grid-connected system according to formula (4): （4）； in, is the grid voltage, is the phase angle of the power grid; Step S24, defining the line equivalent impedance of the wind turbine grid-connected system according to equation (5): （5）； in, is the equivalent impedance of the line; Step S25, based on the grid connection point voltage of the topological structure and the grid voltage, equation (4) and equation (5) are combined to obtain equation (6): （6）； Step S26: multiply both sides of the equation by , to eliminate and get and Based on the deviation angle as well as The expression (7): （7）； Step S27, substitute equation (7) into and , and the reduced-order model (8) is obtained: （8）； in, is the equilibrium point of the topological structure, is the grid angular frequency.

4. The data analysis method according to claim 3, characterized in that: Step S5, defining the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model, including: Step S51, defining the system function (9) of the wind turbine grid-connected system according to the reduced-order model: （9）； in, For the system function Branches, For the system function Branches; Step S52, converting the system function into the Jacobian matrix (10): （10）。 5. The data analysis method according to claim 3, characterized in that: Step S6, defining the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory, including: Step S61, defining the Lyapunov function (11) based on the Lyapunov stability theory: （11）； in, is the Lyapunov function, is the definition domain of the stability domain; Step S62: If there is a Lyapunov function that satisfies the Jacobian matrix, the stability region is defined according to equation (12): （12）。 6. The data analysis method according to claim 5, characterized in that: Step S7, defining the Lyapunov function as a sufficient condition for a subset of the attraction domain of the wind turbine grid-connected system, wherein the sufficient condition includes the stability domain, including: Step S71, defining the sufficient condition according to the Lyapunov stability theory according to formula (13): （13）； in, is the attraction domain subset, For all state variables dimensional vector set, is the gradient operator, for The gradient of is the reduced-order model.

7. The data analysis method according to claim 5, characterized in that: Step S8, iterating the stable domain with the sufficient condition as a constraint by using an optimization algorithm until the stable domain reaches a maximum value, including: Step S81, defining a plurality of random solutions based on the stability domain; Step S82, performing iterative regional constraints based on each random solution through the sufficient condition; Step S83, defining the optimization result of all random solutions as the maximum value of the stable domain; Step S84, initializing the position of each random solution, and updating the current position and current speed of each random solution respectively; Step S85, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update; Step S86, respectively determining whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold, if both are less than, executing step S87; Step S87, respectively determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold, if both are less than, executing step S88; Step S88: determining whether the optimal solution of the stable domain has been obtained.

8. A data analysis device for spatial state expansion of a wind turbine generator set, the data analysis device being applied to the data analysis method according to any one of claims 1 to 7, characterized in that: The data analysis device for the spatial state expansion of the wind turbine generator set comprises: A full-order model building module, used to build a full-order model based on the topological structure of the wind turbine grid-connected system; A reduced-order model building module is used to reduce the order of the full-order model to obtain a reduced-order model; A model virtual scene simulation module simulates grid voltage drop and heavy load switching based on the full-order model and the reduced-order model, respectively, to obtain a full-order output state of the full-order model and a reduced-order output state of the reduced-order model; A reduced-order model accuracy judgment module, used to judge whether the data similarity between the reduced-order output state and the full-order output state is greater than or equal to a preset similarity threshold; A system Jacobian matrix definition module, used to define the Jacobian matrix of the wind turbine grid-connected system according to the reduced-order model based on whether the data similarity is greater than or equal to a preset similarity threshold; A system stability domain definition module, used for defining the stability domain of the Jacobian matrix satisfying the Lyapunov function according to the Lyapunov stability theory; An attraction domain subset definition module, used to define the sufficient condition that the Lyapunov function is an attraction domain subset of the wind turbine grid-connected system, wherein the sufficient condition includes the stability domain; A stable domain iteration module, used for iterating the stable domain by using an optimization algorithm with the sufficient condition as a constraint until the stable domain reaches a maximum value; The maximum estimated attraction domain acquisition module is used to acquire the Lyapunov function matching the maximum value, which is the maximum estimated attraction domain of the wind turbine grid-connected system.

9. An electronic device, characterized in that: It comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements the data analysis method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by the processor, the data analysis method according to any one of claims 1 to 7 can be implemented.

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